A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients

BMC Research Notes

Jeremy C Simpson²  Kathleen M Curran¹  Kenan Handzic²  Vasanth R Singan¹ 

[1] School of Medicine and Medical Science, University College Dublin, Dublin 4, Belfield, Ireland;School of Biology and Environmental Science & Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Belfield, Ireland

关键词: Rab proteins;    Clustering;    Texture features;    Image analysis;    Quantitative co-localization;   

Others  :  1166338
DOI  :  10.1186/1756-0500-5-281

 received in 2012-03-06, accepted in 2012-06-08,  发布年份 2012

【 摘 要 】

Background

The localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization.

Findings

We have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells.

Conclusions

We show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.

【 授权许可】

   
2012 Singan et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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